{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from agots.multivariate_generators.multivariate_data_generator import MultivariateDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x0\n",
       "x0  1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 1\n",
    "K = 0\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x0\n",
       "x0  1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 1\n",
    "K = 1\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.62753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.62753</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x0       x1\n",
       "x0  1.00000  0.62753\n",
       "x1  0.62753  1.00000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 2\n",
    "K = 1\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.985941</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x0        x1\n",
       "x0  1.000000  0.985941\n",
       "x1  0.985941  1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 2\n",
    "K = 2\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
